Fitr: A Platform for Computational Psychiatry Research

Contents

Installation

The current PyPI release of Fitr can be installed as follows:

pip install fitr

If you want the latest version on the GitHub master branch, install as follows:

pip install git+https://github.com/ComputationalPsychiatry/fitr.git

Conceptual Overview

Contents

Intro to Reinforcement Learning

Coming soon…

Modelling Behavioural Data
Pre-Built Models

We have included pre-built models for the following tasks:

Task What it tests Reference
N-Arm Bandit Exploration/Exploitation [Daw2006]
2-Step Task Model-based vs. Model-free control [Daw2011]
References
[Daw2006]Daw, N.D. et al. (2006) Cortical substrates for exploratory decisions in humans. Nature 441, 876–879
[Daw2011]Daw, N.D. et al. (2011) Model-based influences on humans’ choices and striatal prediction errors. Neuron 69, 1204–1215
Fitting Models to Data
Model-Fitting Methods in Fitr

Fitr implements several model-fitting methods:

Method Function Reference
EM with Laplace Approximation fitr.EM() [Huys2011]
Empirical Priors fitr.EmpiricalPriors() [Gershman2016]
Markov Chain Monte-Carlo (Stan) fitr.MCMC() [StanDevs]

Here, “EM” refers to Expectation-Maximization.

References
[Gershman2016]Gershman, S.J. (2016) Empirical priors for reinforcement learning models. J. Math. Psychol. 71, 1–6
[Huys2011]Huys, Q. J. M., et al. (2011). Disentangling the roles of approach, activation and valence in instrumental and pavlovian responding. PLoS Computational Biology, 7(4).
[StanDevs]Stan Development Team. 2016. PyStan: the Python interface to Stan, Version 2.14.0.0. http://mc-stan.org
Selecting the Best Model

Fitr facilitates model-selection via Bayesian Information Criterion (BIC), Aikake Information Criterion (AIC), and Bayesian Model Selection (BMS) [Rigoux2014]. BMS is a re-implementation of spm_BMS() from the Statistical Parametric Mapping toolbox for MATLAB.

References
[Rigoux2014]Rigoux, L. et al. (2014) Bayesian model selection for group studies - Revisited. Neuroimage 84, 971–985

Contributing to Fitr

Your contributions to Fitr are welcome and encouraged. Fitr is being developed on GitHub in order to facilitate improvements by the community. However, to ensure that Fitr develops as a robust piece of software, we have several guidelines for contributions. These have been chosen to mirror those of the SciPy/NumPy project.

Contributions to Fitr should have

  1. Unit tests
    • It is important that Fitr functions well “out of the box,” and this requires that code implemented in Fitr is appropriately tested.
    • Fitr uses Codecov.io to assess code coverage. In general, try to ensure that your new code is covered by unit tests.
    • Unit tests are written in the /tests folder, where you can find examples of how unit tests are currently written.
  2. Documentation
    • New code is not of great use unless the community knows what it is for and how to use it. As such, we ask that any new functions or modifications to existing functions carry the appropriate documentation.
    • If your contribution is substantial, it may be of use to write a tutorial, which are done with Jupyter Notebooks here.
    • Documentation of modules, classes, and functions can be done in Docstrings, then compiled with Sphinx and autodoc.
    • Documentation of Fitr code follows the SciPy/NumPy format
  3. Appropriate code style
    • Fitr follows the PEP8 standard, and so we recommend that you run a linter to ensure that contributions adhere to this format.

Types of Contributions

At this early stage, we are open to any new contributions, big or small.

Many of the contribution requirements listed above were not adhered to at Fitr’s inception, so even if you would like to help by correcting some of our past mistakes, this would be an important step toward Fitr’s goals!

How to Contribute

  1. Fork the GitHub repository
  2. Create a new branch
  3. Submit a pull request

Fitr’s master branch is protected and requires Unit tests to pass, as well as 2 reviews before merging is allowed.

Requesting Features and Reporting Problems

Open an issue on the Fitr GitHub page, and we’ll get on it as soon as possible!

Fitr API

Modules:

Inference

Modules:

Maximum-Likelihood Estimation
class fitr.inference.mle.MLE(loglik_func, params, name='MLEModel')

Maximum Likelihood parameter estimation

Attributes:
name : str

Name of the model being fit. We suggest using the free parameters.

loglik_func : function

The log-likelihood function to be used for model fitting

params : list

List of parameters from the rlparams module

nparams : int

Number of free parameters in the model

param_rng : list

List of strings denoting the parameter ranges (see rlparams module for further details)

Methods

fit(data, n_iterations=1000, opt_algorithm=’BFGS’) Runs model-fitting algorithm
__printfitstart(self, n_iterations, algorithm, verbose) (Private) function to print optimization info to console
fit(data, n_iterations=1000, c_limit=0.0001, opt_algorithm='L-BFGS-B', verbose=True)

Runs the maximum a posterior model-fitting with empirical priors.

Parameters:
data : dict

Dictionary of data from all subjects.

n_iterations : int

Maximum number of iterations to allow.

c_limit : float

Threshold at which convergence is determined

opt_algorithm : {‘L-BFGS-B’}

Algorithm to use for optimization. Only works at present with L-BFGS-B.

verbose : bool

Whether to print progress of model fitting
Returns:
ModelFitResult

Representation of the model fitting results
Expectation-Maximization
class fitr.inference.em.EM(loglik_func, params, name='EMModel')

Expectation-Maximization with the Laplace Approximation [Huys2011], [HuysEMCode].

Attributes:
name : str

Name of the model being fit. We suggest using the free parameters.

loglik_func : function

The log-likelihood function to be used for model fitting

params : list

List of parameters from the rlparams module

nparams : int

Number of free parameters in the model

param_rng : list

List of strings denoting the parameter ranges (see rlparams module for further details)

prior : scipy.stats distribution

The prior distribution over parameter estimates. Here this is fixed to a multivariate normal.

mu : ndarray(shape=nparams)

The prior mean over parameters

cov : ndarray(shape=(nparams,nparams))

The covariance matrix for prior over parameter estimates

Methods

fit(data, n_iterations=1000, c_limit=1, opt_algorithm=’BFGS’, diag=False, verbose=True) Run the model-fitting algorithm
logposterior(x, states, actions, rewards) Computes the log-posterior probability
group_level_estimate(param_est, hess_inv) Updates the hyperparameters of the group-level prior
__printfitstart(self, n_iterations, c_limit, algorithm, init_grid, grid_reinit, dofull, early_stopping, verbose) (Private) function to print optimization info to console
__printupdate(self, opt_iter, subject_i, posterior_ll, verbose) (Private) function to print update on fit iteration to console
fit(data, n_iterations=1000, c_limit=0.001, opt_algorithm='L-BFGS-B', init_grid=False, grid_reinit=True, n_grid_points=5, n_reinit=1, dofull=True, early_stopping=True, verbose=True)

Performs maximum a posteriori estimation of subject-level parameters

Parameters:
data : dict

Dictionary of data from all subjects.

n_iterations : int

Maximum number of iterations to allow.

c_limit : float

Threshold at which convergence is determined

opt_algorithm : {‘BFGS’, ‘L-BFGS-B’}

Algorithm to use for optimization

init_grid : bool

Whether to initialize the optimizer using brute force grid search. If False, will sample from normal distribution with mean 0 and standard deviation 1.

grid_reinit : bool

If optimization does not converge, whether to reinitialize with values from grid search

n_grid_points : int

Number of points along each axis to evaluate during grid-search initialization (only meaningful if init_grid is True).

n_reinit : int

Number of times to reinitialize the optimizer if not converged

dofull : bool

Whether update of the full covariance matrix of the prior should be done. If False, the covariance matrix is limited to one in which the off-diagonal elements are set to zero.

early_stopping : bool

Whether to stop the EM procedure if the log-model-evidence begins decreasing (thereby reverting to the last iteration’s results).

verbose : bool

Whether to print progress of model fitting
Returns:
ModelFitResult

Representation of the model fitting results
group_level_estimate(param_est, hess_inv, dofull, verbose=True)

Updates the group-level hyperparameters

Parameters:
param_est : ndarray(shape=(nsubjects, nparams))

Current parameter estimates for each subject

hess_inv : ndarray(shape=(nparams, nparams, nsubjects))

Inverse Hessian matrix estimate for each subject from the iteration with highest log-posterior probability

dofull : bool

Whether update of the full covariance matrix of the prior should be done. If False, the covariance matrix is limited to one in which the off-diagonal elements are set to zero.

verbose : bool

Controls degree to which results are printed
initialize_opt(fn=None, grid=False, Ns=None)

Returns initial values for the optimization

Parameters:
fn : function

Function over which grid search takes place

grid : bool

Whether to return initialization values from grid search

Ns : int

Number of points per axis over which to evaluate during grid search
Returns:
x0 : ndarray

1 X N vector of initial values for each parameter
logposterior(x, states, actions, rewards)

Represents the log-posterior probability function

Parameters:
x : ndarray(nparams)

Array of parameters for single subject

states : ndarray(shape=[ntrials, nsteps])

Array of states encountered by subject

actions: ndarray(shape=[ntrials, nsteps])

Array of actions taken by subject

rewards : ndarray(shape=[ntrials, nsteps])

Array of rewards received by the subject.
Returns:
float

Log-posterior probability
Empirical Priors
class fitr.inference.empiricalpriors.EmpiricalPriors(loglik_func, params, name='EmpiricalPriorsModel')

Inference procedure with empirical priors

Attributes:
name : str

Name of the model being fit. We suggest using the free parameters.

loglik_func : function

The log-likelihood function to be used for model fitting

params : list

List of parameters from the rlparams module

nparams : int

Number of free parameters in the model

param_rng : list

List of strings denoting the parameter ranges (see rlparams module for further details)

Methods

fit(data, n_iterations=1000, opt_algorithm=’BFGS’) Runs model-fitting algorithm
logposterior(x, states, actions, rewards) Computes the log-poseterior probability
__printfitstart(self, n_iterations, algorithm, verbose) (Private) function to print optimization info to console
__printupdate(self, opt_iter, subject_i, posterior_ll, verbose) (Private) function to print iteration info to console
fit(data, n_iterations=1000, c_limit=0.001, opt_algorithm='L-BFGS-B', verbose=True)

Runs the maximum a posterior model-fitting with empirical priors.

Parameters:
data : dict

Dictionary of data from all subjects.

n_iterations : int

Maximum number of iterations to allow.

c_limit : float

Threshold at which convergence is determined

opt_algorithm : {‘L-BFGS-B’}

Algorithm to use for optimization. Only works at present with L-BFGS-B.

verbose : bool

Whether to print progress of model fitting
Returns:
ModelFitResult

Representation of the model fitting results
logposterior(x, states, actions, rewards)

Represents the log-posterior probability function

Parameters:
x : ndarray(nparams)

Array of parameters for single subject

states : ndarray

Array of states encountered by subject. Number of rows should reflect number of trials. If the task is a multi-step per trial task, then the number of columns should reflect the number of steps, unless a custom likelihood function is used which does not require this.

actions: ndarray

Array of actions taken by subject. Number of rows should reflect number of trials. If the task is a multi-step per trial task, then the number of columns should reflect the number of steps, unless a custom likelihood function is used which does not require this.

rewards : ndarray

Array of rewards received by the subject. Number of rows should reflect number of trials. If there are multiple steps at which rewards are received, they should be stored in different columns, unless a custom likelihood funciton is used which does not require this.
Returns:
float

Log-posterior probability
Markov-Chain Monte-Carlo
class fitr.inference.mcmc.MCMC(generative_model=None, name='FitrMCMCModel')

Uses Markov-Chain Monte-Carlo (via PyStan) to estimate models

Attributes:
name : str

Name of the model being fit

generative_model : GenerativeModel object

Methods

fit(self, data, chains=4, n_iterations=2000, warmup=None, thin=1, seed=None, init=’random’, sample_file=None, algorithm=’NUTS’, control=None, n_jobs=-1, compile_verbose=False, sampling_verbose=False) Runs the MCMC Inference procedure with Stan
__initresults(self) (Private) method to initialize MCMCFitResult object
fit(data, chains=4, n_iterations=2000, warmup=None, thin=1, seed=None, init='random', sample_file=None, algorithm='NUTS', control=None, n_jobs=-1, compile_verbose=False, sampling_verbose=False)

Runs the MCMC Inference procedure with Stan

Parameters:
data : dict

Subject level data

chains : int > 0

Number of chains in sampler

n_iter : int

How many iterations each chain should run (includes warmup)

warmup : int > 0, iter//2 by default

Number of warmup iterations.

thin : int > 0

Period for saving samples

seed : int or np.random.RandomState, optional

Positive integer to initialize random number generation

sample_file : str

File name specifying where samples for all parameters and other saved quantities will be written. If None, no samples will be written

algorithm : {‘NUTS’, ‘HMC’, ‘Fixed_param’}, optional

Which of Stan’s algorithms to implement

control : dict, optional

Dictionary of parameters to control sampler’s behaviour (see PyStan documentation for details)

n_jobs : int, optional

Sample in parallel. If -1, all CPU cores are used. If 1, no parallel computing is used

compile_verbose : bool

Whether to print output from model compilation

sampling_verbose : bool

Whether to print intermediate output from model sampling
Returns:
ModelFitResult

Instance containing model fitting results

References

[1]PyStan API documentation (https://pystan.readthedocs.io)
Fitmodel: High Level Model-Fitting Wrapper
class fitr.inference.fitmodel.FitModel(name='Anon Model', loglik_func=None, params=None, generative_model=None)

An object representing a model to be fit to behavioural data. This should be viewed as a high level wrapper for multiple potential model fitting algorithms which themselves can be run by using their respective classes.

Attributes:
name : str

Name of the model. We suggest identifying model based on free parameters.

loglik_func : function

The log-likelihood function to be used to fit the data

params : list

List of reinforcement learning parameter objects from the rlparams module.

generative_model : GenerativeModel object

Object representing a generative model

Methods

fit(data, method=’EM’, c_limit=0.01) Runs the specified model fitting algorithm with the given data.
fit(data, method='EM', c_limit=0.01, verbose=True)

Runs model fitting

Parameters:
data : dict

Behavioural data.

method : {‘EM’, ‘MLE’, ‘EmpiricalPriors’, ‘MCMC’}

The inference algorithm to use. Note that the data formats for ‘MCMC’ compared to the other methods is distinct, and should correspond appropriately to the method being employed

c_limit : float

Limit at which convergence of log-posterior probability is determined (only for methods ‘EM’ and ‘EmpiricalPriors’)

verbose : bool

Controls amount of printed output during model fitting
Returns:
fitrfit : object

Representation of the model fitting results
ModelFitResult
class fitr.inference.modelfitresult.MCMCFitResult(method, nsubjects, nparams, name)

Results of model fitting with MCMC

Attributes:
name : str

Model identifier. We suggest using free-parameters as identifiers

method : str

Method employed in optimization.

nsubjects : int

Number of subjects fitted.

nparams : int

Number of free parameters in the fitted model.

params : ndarray(shape=(nsubjects, nparams))

Array of parameter estimates

paramnames : list

List of parameter names

stanfit :

Stan fit object

summary : pandas.DataFrame

Summary of the MCMC fit results

Methods

get_paramestimates(self, FUN=np.mean) Extracts parameter estimates
trace_plot(self, figsize=None, save_figure=False, filename=’fitr-mcstan-traceplot.pdf’) Trace plot for fit results
get_paramestimates(FUN=<Mock name='mock.median' id='139918001903096'>)

Extracts parameter estimates

Parameters:
FUN : {numpy.mean, numpy.median}
make_summary()

Creates summary of Stan fitting results

trace_plot(figsize=None, save_figure=False, filename='fitr-mcstan-traceplot.pdf')

Easy wrapper for Stan Traceplot

Parameters:
figsize : (optional) list [width in inches, height in inches]

Controls figure size

save_figure : bool

Whether to save the figure to disk

filename : str

The file name to be output
class fitr.inference.modelfitresult.ModelFitResult(method, nsubjects, nparams, name=None)

Class representing the results of a fitrmodel fitting.

Attributes:
name : str

Model identifier. We suggest using free-parameters as identifiers

method : str

Method employed in optimization.

nsubjects : int

Number of subjects fitted.

nparams : int

Number of free parameters in the fitted model.

params : ndarray(shape=(nsubjects, nparams))

Array of parameter estimates

paramnames : list

List of parameter names

Methods

set_paramnames(params) Sets names of RL parameters to the fitrfit object
plot_ae(actual, save_figure=False, filename=’actual-estimate.pdf’) Plots estimated parameters against actual simulated parameters
summary_table(write_csv=False, filename=’summary-table.csv’, delimiter=’,’) Writes a CSV file with summary statistics from the present model
ae_metrics(actual, matches=None)

Computes metrics summarizing the ability of the model to fit data generated from a known model

Parameters:
matches : list

List consisting of [rlparams object, column index in actual, column index in estimates]. Ensures comparisons are being made between the same parameters, particularly when the models have different numbers of free parameters.
Returns:
DataFrame

Including summary statistics of the parameter matching
plot_ae(actual, save_figure=False, filename='actual-estimate.pdf')

Plots actual parameters (if provided) against estimates

Parameters:
actual : ndarray(shape=(nsubjects, nparams))

Array of actual parameters from a simulation

save_figure : bool

Whether to save the figure to disk

filename : str

The file name to be output
set_paramnames(params)

Sets the names of the RL parameters to the fitrfit object

Parameters:
params : list

List of parameters from the rlparams module
class fitr.inference.modelfitresult.OptimizationFitResult(method, nsubjects, nparams, name)

Results of model fitting with optimization methods

Attributes:
name : str

Model identifier. We suggest using free-parameters as identifiers

method : str

Method employed in optimization.

nsubjects : int

Number of subjects fitted.

nparams : int

Number of free parameters in the fitted model.

params : ndarray(shape=(nsubjects, nparams))

Array of parameter estimates

paramnames : list

List of parameter names

errs : ndarray(shape=(nsubjects, nparams))

Array of parameter estimate errors

nlogpost : ndarray(shape=(nsubjects))

Subject level negative log-posterior probability

nloglik : float

Subject level negative log-likelihood

LME : float

Log-model evidence

BIC : ndarray(shape=(nsubjects))

Subject-level Bayesian Information Criterion

AIC : ndarray(shape=(nsubjects))

Subject-level Aikake Information Criterion

summary : DataFrame

Summary of means and standard deviations for each free parameter, as well as negative log-likelihood, log-model-evidence, BIC, and AIC for the model

Methods

plot_fit_ts(save_figure=False, filename=’fit-stats.pdf’) : Plots the evolution of log-likelihood, log-model-evidence, AIC, and BIC over optimization iterations
param_hist(save_figure=False, filename=’param-hist.pdf’) : Plots hitograms of parameters in the model
summary_table(write_csv=False, filename=’summary-table.csv’, delimiter=’,’) Writes a CSV file with summary statistics from the present model
param_hist(save_figure=False, filename='param-hist.pdf')

Plots histograms of the parameter estimates

Parameters:
save_figure : bool

Whether to save the figure to disk

filename : str

The file name to be output
plot_fit_ts(save_figure=False, filename='fit-stats.pdf')

Plots the log-model-evidence, BIC, and AIC over optimization iterations

Parameters:
save_figure : bool

Whether to save the figure to disk

filename : str

The file name to be output
summary_table()

Generates a table summarizing the model-fitting results

Model Selection

Modules:

Aikake Information Criterion Model-Selection
Bayesian Information Criterion Model-Selection
Bayesian Model Selection
Cross Validation Methods

Functions for cross validation

class fitr.criticism.cross_validation.LOACV(cv_func)

Look-one-ahead cross validation

Attributes:
cv_func : loacv function

A look-one-ahead cross-validation function from a Fitr model

results : LookOneAheadCVResult

Stores results of the cross validation

Methods

run(params, data) Runs the Look-One-Ahead cross validation
run(params, data)

Runs the Look-One-Ahead cross validation

Parameters:
params : ndarray(shape=(nsubjects, nparams))

Array of parameters

data : dict

Behavioural data in Fitr OptimizationData format
class fitr.criticism.cross_validation.LookOneAheadCVResult(params)

Stores and manipulates results of a Look-One-Ahead cross-validation run

Attributes:
nsubjects : dict

Dictionary of

accuracy : dict

Dictionary of accuracy values (overall and by subject)

raw : dict

Dictionary

Methods

accuracy_hist([save_figure, filename, figsize]) Plots moving average of accuracy
accuracy_maplot([save_figure, filename, figsize]) Plots moving average of accuracy
accuracy_param_scatter([paramnames, ylim, …]) Plots accuracy against parameter values.
accuracy_hist(save_figure=False, filename='accuracy-hist.pdf', figsize=None)

Plots moving average of accuracy

Parameters:
save_figure : bool

Whether to save the plot

filename : str

Name of the file to which figure will be saved

figsize : (optional) tuple (width, height)

The size of the figure
accuracy_maplot(save_figure=False, filename='accuracy-maplot.pdf', figsize=None)

Plots moving average of accuracy

Parameters:
save_figure : bool

Whether to save the plot

filename : str

Name of the file to which figure will be saved

figsize : (optional) tuple (width, height)

The size of the figure
accuracy_param_scatter(paramnames=None, ylim=None, alpha=0.5, save_figure=False, filename='accuracy-param-scatter.pdf', figsize=None)

Plots accuracy against parameter values. Helpful to visually inspect the effects of various parameters on cross-validation accuracy

Parameters:
paramnames : (optional) list

List of parameter names in strings

ylim : (optional) tuple (min, max)

Y-axis limits

alpha : 0 < float < 1

Transparency of the plot points

save_figure : bool

Whether to save the plot

filename : str

Name of the file to which figure will be saved

figsize : (optional) tuple (width, height)

The size of the figure
Returns:
matplotlib.pyplot.figure
Model-Selection Result
class fitr.criticism.modelselectionresult.ModelSelectionResult(method)

Object containing results of model selection

Attributes:
modelnames : list

List of strings labeling models

xp : ndarray

Exceedance probabilities for each model

pxp : ndarray

Protected exceedance probabilities for each model

BIC : ndarray

Bayesian information criterion measures for each model

AIC : ndarray

Aikake information criterion measures for each model

Methods

plot(self, statistic, save_figure=False, filename=’modelselection-plot.pdf’, figsize=(10, 10)) Plots the results of model selection (bars)
plot(statistic, save_figure=False, filename='modelselection-plot.pdf', figsize=(10, 10))

Plots the results of model selection (bars)

Parameters:
statistic : {‘pxp’, ‘xp’, ‘BIC’, ‘AIC’}

Which statistic is desired for the bar plot

save_figure : bool

Whether to save the figure

filename : str

The desired filename for the plot (must end in appropriate extension)

figsize : tuple, default (10, 10)

Task Models and Data Objects

Modules:

Synthetic Data
class fitr.models.synthetic_data.SyntheticData

Object representing synthetic data

Attributes:
data : dict

Dictionary containing data formatted for fitr’s model fitting tools (except MCMC via Stan)

data_mcmc : dict

Dictionary containing task data formatted for use with MCMC via Stan

params : ndarray(shape=(nsubjects X nparams))

Subject parameters

groupnames : list

Strings representing names of groups whose data are represented

Methods

append_group(self, data=SyntheticData)  
get_nparams(self) Returns the number of parameters in the data
get_nsubjects(self) Returns the number of subjects in the data
cumreward_param_plot(self, alpha=0.9) Plots the cumulative reward against model parameters. Useful to determine the relationship between reward acquisition and model parameters for a given task.
plot_cumreward(self) Plots the cumulative reward over time for each subject
append_group(data, which='all')

Appends data from other groups to the SyntheticData object

Parameters:
data : SyntheticData object
all : {‘all’, ‘opt’, ‘mcmc’}

Whether to append all data, optimization data only, or MCMC data
cumreward_param_plot(alpha=0.9, save_figure=False, filename='cumreward-param-plot-sim.pdf')

Plots parameter values against cumulative reward

Parameters:
save_figure : bool

Whether to save the figure to disk

filename : str

The name of the file to which to save the figure
get_nparams()

Finds the number of parameters in the model

Returns:
int
get_nsubjects()

Finds the number of subjects in the data

Returns:
int
plot_cumreward(save_figure=False, filename='cumreward-plot-sim.pdf')

Plots cumulative reward over time for each subject

Parameters:
save_figure : bool

Whether to save the figure to disk

filename : str

The name of the file to which to save the figure
fitr.models.synthetic_data.combine_groups(x, y)

Combines synthetic data objects for multiple groups

Parameters:
x : SyntheticData

Data for first simulated group

y : SyntheticData

Data for second simulated group
Returns:
SyntheticData

Combined groups

Plotting Functions

Modules:

Distance Metric Plots

Plotting functions for distance metrics

Module Documentation
fitr.plotting.distance.distance_hist(X, group_labels, xlab='Distance', ylab='', normed=1, alpha=0.5, save_figure=False, figsize=None, figname='distance-hist.pdf')

Creates a histogram of within- and between-group distances.

Parameters:
group_labels : ndarray(size=n_labels)

Vector of group labels for each participant represented

xlab : str

X-axis label

ylab : str

Y-axis label

normed : 0 or 1 (default=1)

Whether the histogram should be normalized

alpha : float on interval (0, 1)

Transparency of scatterplot points

save_figure : bool

Whether to save the figure

figsize : (optional) list

Controls figure size

figname : str

The name under which the plot should be saved
fitr.plotting.distance.distance_scatter(X, Y, group_labels=None, xlab='', ylab='', alpha=0.5, save_figure=False, figsize=None, figname='distance-scatter.pdf')

Creates a scatterplot between two distance metrics, demonstrating group separation, if any.

Parameters:
group_labels : (optional)
xlab : str

X-axis label

ylab : str

Y-axis label

alpha : float on interval (0, 1)

Transparency of scatterplot points

save_figure : bool

Whether to save the figure

figsize : (optional) list

Controls figure size

figname : str

The name under which the plot should be saved
ParamPlots

Various plotting functions for parameter estimates

Module Documentation
fitr.plotting.paramplots.param_scatter(X, Y, paramnames=None, xlabel='Parameter Value', ylabel='y value', ylim=None, alpha=0.5, figsize=None, save_figure=False, filename='param-scatter.pdf')

Plots a value against parameter estimates for each parameter

Parameters:
X : ndarray(shape=(nsubjects, nparams))

Parameter array

Y : ndarray(shape=nsubjects)

Value to be plotted against parameters

paramnames : (optional) list

Parameter names (will be the title for each plot)

xlabel : str

Label for x axis

ylabel : str

Label for y axis

ylim : (optional) tuple (min, max)

Y-axis limits

alpha : 0 < float < 1

Transparency of scatter points

figsize : (optional) tuple (width, height)
save_figure : bool

Whether to save the plot

filename : str

Path to which to plot the figure
Returns:
matplotlib.pyplot.figure

RLParams

Module containing commonly used reinforcement learning parameter objects.

Module Documentation
class fitr.rlparams.ChoiceRandomness(name='Choice Randomness', rng='pos', mean=4, sd=1)

An choice randomness parameter object

Attributes:
name : str

Name of the parameter. To be used for plots and so forth.

rng : {‘unit’, ‘pos’, ‘neg’, ‘unc’}

The domain over which the parameter lies (unit=[0,1], pos=[0,+Inf], neg=[-Inf,0], unc=[-Inf, +Inf])

dist : scipy.stats.gamma distribution

Methods

sample(size=1) Samples from the parameter’s distribution
class fitr.rlparams.EligibilityTrace(name='Eligibility Trace', rng='unit', mean=0.5, sd=0.27)

An eligibility trace parameter object.

Attributes:
name : str

Name of the parameter. To be used for plots and so forth.

rng : {‘unit’, ‘pos’, ‘neg’, ‘unc’}

The domain over which the parameter lies (unit=[0,1], pos=[0,+Inf], neg=[-Inf,0], unc=[-Inf, +Inf])

dist : scipy.stats.beta distribution

Methods

sample(size=1) Samples from the parameter’s distribution
class fitr.rlparams.LearningRate(name='Learning Rate', rng='unit', mean=0.5, sd=0.27)

A learning rate object.

Attributes:
name : str

Name of the parameter. To be used for plots and so forth.

rng : {‘unit’, ‘pos’, ‘neg’, ‘unc’}

The domain over which the parameter lies (unit=[0,1], pos=[0,+Inf], neg=[-Inf,0], unc=[-Inf, +Inf])

dist : scipy.stats.beta distribution

Methods

sample(size=1) Samples from the parameter’s distribution
class fitr.rlparams.MBMF_Balance(name='Model-Based Control Weight', rng='unit', mean=0.5, sd=0.27)

An object representing the parameter that balances model-based and model-free control.

Attributes:
name : str

Name of the parameter. To be used for plots and so forth.

rng : {‘unit’, ‘pos’, ‘neg’, ‘unc’}

The domain over which the parameter lies (unit=[0,1], pos=[0,+Inf], neg=[-Inf,0], unc=[-Inf, +Inf])

dist : scipy.stats.beta distribution

Methods

sample(size=1) Samples from the parameter’s distribution
class fitr.rlparams.Param(name=None, rng=None)

A base parameter object that can be used to generate new parameters.

Attributes:
name : str

Name of the parameter. To be used for plots and so forth.

rng : {‘unit’, ‘pos’, ‘neg’, ‘unc’}

The domain over which the parameter lies (unit=[0,1], pos=[0,+Inf], neg=[-Inf,0], unc=[-Inf, +Inf])

Methods

sample(size=1) Samples from the parameter’s distribution
convert_meansd(mean, sd, dist)

Converts mean and standard deviation to other distribution parameters.

Parameters:
mean : float

Mean value for distribution (must lie within region of support)

sd : float

Standard deviation for distribution

dist : {‘beta’, ‘gamma’}

Target distribution

Notes

Currently, only the gamma and beta distributions are supported for this function.

The Beta distribution has two shape parameters \(\lbrace \alpha, \beta \rbrace > 0\). Using the mean \(\mu\) and the standard deviation \(\sigma\), the \(\alpha\) parameter can be calculated as

\[\alpha = (\frac{1-\mu}{\sigma^2} - \frac{1}{\mu})\mu^2\]

and the \(\beta\) parameter as

\[\beta = \alpha (\frac{1}{\mu} - 1)\]

Note that for the Beta distribution to be defined this way, the following constraint must hold for the mean, \(0 < \mu < 1\), and the following for the variance, \(0 < \sigma^2 \leq \mu - \mu^2\).

For the Gamma distribution, we have a shape parameter \(\kappa > 0\) and a scale parameter \(\theta\). These can be calculated using the mean \(\mu\) and standard deviation \(\sigma\) as

\[\theta = \frac{\sigma^2}{\mu}\]

and

\[\kappa = \frac{\mu^2}{\sigma^2}\]
plot_pdf(xlim=None, figsize=None, save_figure=False, filename='parameter-pdf.pdf')

Plots the probability density function of this parameter

Parameters:
xlim : (optional) list of lower and upper bounds of x axis
figsize : (optional) list defining plot dimensions
save_figure : bool

Whether to save the figure at function call

filename : str

The name of the file at which to save the figure
sample(size=1)

Samples from the parameter’s distribution

Parameters:
size : int

Number of samples to draw
Returns:
ndarray
class fitr.rlparams.Perseveration(name='Perseveration', rng='unc', mean=0.0, sd=0.1)

An perseveration parameter object

Attributes:
name : str

Name of the parameter. To be used for plots and so forth.

rng : {‘unit’, ‘pos’, ‘neg’, ‘unc’}

The domain over which the parameter lies (unit=[0,1], pos=[0,+Inf], neg=[-Inf,0], unc=[-Inf, +Inf])

dist : scipy.stats.norm distribution

Methods

sample(size=1) Samples from the parameter’s distribution
class fitr.rlparams.RewardSensitivity(name='Reward Sensitivity', rng='unit', mean=0.5, sd=0.27)

A reward sensitivity object.

Attributes:
name : str

Name of the parameter. To be used for plots and so forth.

rng : {‘unit’, ‘pos’, ‘neg’, ‘unc’}

The domain over which the parameter lies (unit=[0,1], pos=[0,+Inf], neg=[-Inf,0], unc=[-Inf, +Inf])

dist : scipy.stats.beta distribution

Methods

sample(size=1) Samples from the parameter’s distribution

Utils

Module containing functions that are used across Fitr modules

References
[R98b6f8855756-Akam2015]Akam, T. et al. (2015) Simple Plans or Sophisticated Habits? State, Transition and Learning Interactions in the Two-Step Task. PLoS Comput. Biol. 11, 1–25
Module Documentation
fitr.utils.action(x)

Selects an action based on state-action values

Parameters:
x : ndarray

Array of action values (scaled by inverse softmax temperature).
Returns:
int

The index corresponding to the selected action

Notes

This function computes the softmax probability for each action in the input array, and subsequently samples from a multinomial distribution parameterized by the results of the softmax computation. Finally, it returns the index where the value is equal to 1 (i.e. which action was selected).

fitr.utils.logsumexp(x)

Numerically stable logsumexp.

Parameters:
x : ndarray(shape=(nactions))
Returns:
float

Notes

The numerically stable log-sum-exp is computed as follows:

\[\max X + \log \sum_X e^{X - \max X}\]
fitr.utils.softmax(x)

Computes numerically stable softmax

Parameters:
x : ndarray(shape=(nactions))
Returns:
ndarray(shape=(nactions))

Softmax probabilities for each action
fitr.utils.trans_UC(values_U, rng)

Transforms parameters from unconstrained to constrained space

Parameters:
values_U : ndarray

Parameter values

rng : {‘unit’, ‘pos’, ‘half’, ‘all_unc’}

The constrained range of the parameter
Returns:
ndarray(shape=(nparams))

Notes

This code was taken from that published along with [Akam2015].

Overview

In decision-making tasks, it is often of interest to understand the psychological mechanisms by which a subject makes choices. One way to approach this problem is by specifying a reinforcement learning (RL) model that describes those mechanisms, and then fitting it (by tuning its free parameters) to subjects’ actual behavioural data. There are many ways to perform this analysis, which for the most part might be inaccessible to researchers without extensive mathematical training. We are building Fitr as an attempt to make this process simpler to implement.

For researchers interested in the computational aspects of decision-making research, Fitr also aims to offer pre-packaged routines to streamline the study & development of new tasks, models, and model-fitting/selection procedures.

Goals

  1. Offer a free and open platform upon which researchers can easily
    • Build and validate behavioural tasks
    • Build and validate behavioural models
    • Develop new model-fitting procedures
    • Fit models to behavioural data from subjects in vivo
  2. Implement the state of the art methods for behavioural modelling studies in computational psychiatry
  3. Integrate well with tools for collecting behavioural data
  4. Integrate well with tools for collecting neurophysiological data

Guiding Principles

  1. Accessibility
    • Fitr should be open-source, free, and not dependent on commercial tools
  2. Parsimony
    • Build tools that can turn data into results with minimal coding
  3. Modularity
    • Build modules, classes, and functions in a way that facilitates the computational modelling workflow as it applies to
      • Developing and validating tasks
      • Developing and validating models
      • Fitting/selecting models using data from human subjects
  4. Flexibility
    • There are many ways to fit a model. Users should be able to easily test multiple models and multiple fitting methods without much additional code.
    • Allow users to easily integrate their own code, where desired
    • Facilitate development of “Pipelines” for Computational Psychiatry research
  5. Don’t re-invent the wheel (unless you have to)
    • If excellent open-source tools exist, don’t rebuild them. Rather, make it possible for users to integrate them easily into their workflow
    • Always give credit wherever credit is due
  6. Build for communication and reproducibility
    • Make it easy for researchers to generate the high-quality tables and plots necessary to communicate the results of their modelling studies.
    • Make it easy to reproduce results generated by Fitr pipelines

What we’re working on

  • Adding new tasks and new models
  • Writing more tutorials
  • End-to-end model-fitting and model-selection
  • Improving existing model-fitting algorithms
  • Adding new model-fitting algorithms (Variational Bayes)
  • Model-based neuroimaging

Citing Fitr

Let us know if you publish a paper using Fitr and we will post it here. If you use Fitr in your work, please cite it so that we can (A) know how people have been using it, and (B) support further funding of our work.

Indices and tables